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Enregistrement W3157346880 · doi:10.1002/sim.8944

Comment on Ellenberg and Morris: The role of statisticians in vaccine surveillance

2021· letter· en· W3157346880 sur OpenAlexaffabout
Robert W. Platt

Notice bibliographique

RevueStatistics in Medicine · 2021
Typeletter
Langueen
DomaineSocial Sciences
ThématiqueVaccine Coverage and Hesitancy
Établissements canadiensMcGill University
Organismes subventionnairesnon disponible
Mots-clésPandemicVaccine trialVaccinationMedicineObservational studyScope (computer science)Vaccine efficacyCoronavirus disease 2019 (COVID-19)VirologyComputer scienceDisease

Résumé

récupéré en direct d'OpenAlex

Ellenberg and Morris 1 illustrate nicely the similarities and contrasts between the HIV and COVID-19 pandemics, and the roles of statisticians in related research.The article ends with the exciting news of emergency use authorization for the two new mRNA-based vaccines that appear extraordinarily effective against COVID-19.This news, and the fact that there are now several vaccines either approved or close to being so in several countries, 2 represents one of the most significant differences between the two pandemics.The delivery and use of coronavirus vaccines present another challenge in which biostatisticians will be implicated in an unprecedented way.We are at the beginning of perhaps the largest vaccine distribution, and therefore the largest vaccine surveillance project, ever undertaken.The worldwide scope and rapidity of the distribution effort means that any research on early vaccines needs to be similarly rapid and of a massive scope, and there are a number of novel aspects of this vaccination effort in which statisticians will be important contributors.Vaccine surveillance typically works through surveillance of spontaneous adverse events collected by self-report (VAERS), and more focused epidemiologic studies to assess potential causal associations between a specific vaccine regimen and an adverse event.3 Observational studies of vaccine efficacy can assess real-world effectiveness against endpoints studied in trials, such as severity, but also important endpoints that were not studied in trials, such as the degree to which the vaccines can prevent transmission.What is novel about the Coronavirus pandemic and vaccine effort?The vaccine effort already includes 10 different vaccines, approved in multiple countries.2 The size of the effort, and its rapidity (over 160 million doses administered already, 4 with over 10 billion doses and 10 different vaccines promised by the end of 2021 2 ) is unprecedented.Finally, vaccines are being administered following a range of dosing strategies.Both the Pfizer and Moderna vaccines were approved for two-dose regimens with the second dose delivered in a fixed time frame.Several countries are considering delaying the second dose, or even single-dose regimens for these vaccines.4 While there is some evidence that single-dose regimens are effective, and that delaying the second dose does not affect vaccine efficacy, these strategies have not been studied in large-scale randomized trials.Observational studies will be the only way we can assess the safety and efficacy of the various vaccines, and the various different regimens, on a large scale.3,5,6 What are the challenges in conducting such surveillance?First, and foremost, systematic data collection are critical.Studies of vaccine safety and effectiveness depend on knowing dosing dates, type of vaccine used, and both history and follow-up.On a large scale, this is only feasible via linkage of accurate vaccine information to health administrative or electronic health record data.7 This effort will require coordination across jurisdictions and across data sources.Despite the massive scale of the vaccine effort, there still will be rare adverse events that will require very large sample sizes to rule out important associations, and any one country may have insufficient sample size, and/or insufficient variation in vaccine type, to detect differences.Distinguishing between real and spurious associations will likely require data on the worldwide scale.This will certainly require multidatabase efforts involving multiple countries and health care providers.Ensuring that data are standardized across jurisdictions, and analyses are coordinated across databases, will require input from statisticians throughout the process.Measurement and recording of outcomes, study design, assessment and control of confounding, and analysis to assess representativeness and transportability should all be considered.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,038
score de la tête « metaresearch » (Gemma)0,179
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,090
Score d'incertitude au seuil0,202

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0380,179
Méta-épidémiologie (sens strict)0,0020,003
Méta-épidémiologie (sens large)0,0040,004
Bibliométrie0,0020,003
Études des sciences et des technologies0,0060,015
Communication savante0,0070,017
Science ouverte0,0110,003
Intégrité de la recherche0,0900,126
Charge utile insuffisante (le modèle a refusé de juger)0,0080,010

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,017
Tête enseignante GPT0,309
Écart entre enseignants0,292 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission2
Résumé présentoui

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